library(coda)
library(bayesplot)
library(ggplot2)
library(ggsci)
library(khroma)
library(tidyverse)
library(reshape2)
library(here)
knitr::opts_chunk$set(echo = TRUE, dpi = 300 )
Set up MCSim file
# this markdown file must be saved in top level directory for the following to work; the mcsim code depends on getwd results.
mdir <- "MCSim"
source(here::here(mdir,"setup_MCSim.R"))
# Make mod.exe (used to create mcsim executable from model file)
makemod()
The mod.exe had been created.
id_lut <- multicheck$df_check %>% select(Level) %>% unique () %>%
mutate(dataset = c(
rep("Decatur M Train", 9),
rep("Decatur F Train", 9),
rep("Decatur M Test", 9),
rep("Decatur F Test", 10),
rep("Minnesota Train", 49),
rep("Minnesota Test", 49),
'Paulsboro-Train','Horsham-Train',
'Warminster-Test','Warrington-Train'),
Sex = c(
rep("M", 9),
rep("F", 9),
rep("M", 9),
rep("F", 10),
rep("Mixed", 49),
rep("Mixed", 49),
rep("Mixed", 4)),
City = c(
rep("Decatur", 18),
rep("Decatur", 19),
rep("Minnesota", 98),
'Paulsboro','Horsham','Warminster','Warrington'),
Train_Test = c(
rep("Train", 9),
rep("Train", 9),
rep("Test", 9),
rep("Test", 10),
rep("Train", 49),
rep("Test", 49),
'Train','Train',
'Test','Test'),
datatype = c(
rep("Individual",9+9+9+10+49+49),
rep("Summary",4)),
Simulation = row_number(),
variable = paste0(dataset, " ",Simulation))
id_lut$dataset <- factor(id_lut$dataset,levels=
c("Decatur M Train","Decatur F Train","Arnsberg M Train",
"Arnsberg F Train","Decatur M Test","Decatur F Test","Arnsberg M Test",
"Arnsberg F Test","Minnesota Train","Minnesota Test",
'Lubeck-Bartell-Train', 'Lubeck-Bartell-Test',
'Little Hocking-Bartell-Train', 'Little Hocking-Bartell-Test',
'Little Hocking-Emmett-Test','Paulsboro-Train','Horsham-Train',
'Warminster-Test','Warrington-Train'))
id_lut$City <- factor(id_lut$City,levels =
c("Decatur","Arnsberg","Minnesota",'Lubeck-Bartell',
'Little Hocking-Bartell','Little Hocking-Emmett',
'Paulsboro','Horsham','Warminster','Warrington'))
indiv_lut <- id_lut %>%
filter(City %in% c("Decatur", "Minnesota")) %>%
mutate( dataset = as.factor(dataset))
nv <- data.frame(dataset =unique(indiv_lut$dataset),
variable= rep("Pop GM", 6),
type= rep("Pop GM", 6), stringsAsFactors = FALSE)
This is a Figure 2 panel. Needed to use “scale=1.1” in ggsave to match PFOA.
nrow(multicheck$df_check)
[1] 88000
nrow(id_lut)
[1] 139
multicheck$df_check %>% left_join(id_lut) %>% nrow()
Joining, by = c("Level", "Simulation")
[1] 88000
names(multicheck$df_check)
[1] "Level" "Simulation" "Output_Var" "Time" "Data"
[6] "Prediction"
Level
Simulation
Output_Var
Time
Data
Prediction
multicheck2 <- multicheck$df_check %>% left_join(id_lut)%>%
group_by_at ( vars(-Prediction)) %>%
summarise(Prediction = median(Prediction)) %>%
ungroup() %>%
group_by(City) %>%
mutate(Train_Test = factor(Train_Test, levels = c("Train", "Test")),
`City (datatype)` = factor (paste0(City, "\n(", datatype, ")\n") ),
label = case_when(Train_Test=="Train" ~ "C: PFOS Train",
Train_Test=="Test" ~"D: PFOS Test",
TRUE ~ ""))
Joining, by = c("Level", "Simulation")
Warning in mutate_impl(.data, dots, caller_env()): Unequal factor levels:
coercing to character
Warning in mutate_impl(.data, dots, caller_env()): binding character and factor
vector, coercing into character vector
Warning in mutate_impl(.data, dots, caller_env()): binding character and factor
vector, coercing into character vector
Warning in mutate_impl(.data, dots, caller_env()): binding character and factor
vector, coercing into character vector
Warning in mutate_impl(.data, dots, caller_env()): binding character and factor
vector, coercing into character vector
Warning in mutate_impl(.data, dots, caller_env()): binding character and factor
vector, coercing into character vector
Warning in mutate_impl(.data, dots, caller_env()): binding character and factor
vector, coercing into character vector
#define color for testing boxplots
bp_cols <- c (as.character (khroma::colour("muted")(9)) , "#191919")
bp_cols <-bp_cols[c(1,3, 7, 10:8)]# plot_scheme_colourblind(bp_cols)
### Create aesthetics lookup
aes_lut <- multicheck2 %>% ungroup() %>%
group_by(City, datatype, `City (datatype)` ) %>% summarise () %>% ungroup() %>%
mutate( cols = bp_cols, city_fills = bp_cols ,
# for individual level on point plot (multicheck2), darken outlines for visibility, use standard colors otherwise
city_outlines = if_else(datatype == "Individual" , colorspace::darken(city_fills, 0.3), city_fills) ,
shapes = case_when(datatype == "Individual" & `City` %in% c('Decatur', 'Arnsberg', 'Minnesota') ~ 23,
datatype == "Summary" &`City` %in% c("Horsham", "Warminster", "Warrington") ~ 2,
datatype == "Summary" & `City` == "Paulsboro" ~ 1,
TRUE ~ 18 ),
size = if_else(datatype =="Individual", 1.75, 2.5 ) )
source( paste0(gsub(basename(here()), 'shared_functions', here()), '/plot_scatter_mcheck.r'))
p2 <- plot_scatter_mcheck(dframe = multicheck2, pfas_nom = pfas_name, aes_lut_fn = aes_lut )
print(p2)
ggsave(here ("output-plots", paste0( sa,"multicheckplot_", pfas_name,
".pdf")),p2,dpi=600, scale=1.1)
Saving 8.8 x 3.85 in image
df_check <- multicheck$df_check
df_check <- subset(df_check,Data > 0)
n1 <- nrow(df_check)
id_chks <- df_check %>% select(Level) %>% unique() %>% bind_cols(id_lut) %>%
mutate(dataset = as.factor(dataset), Sex = as.factor(Sex), City = as.factor(City),
Train_Test = as.factor(Train_Test))
df_check <- df_check %>% left_join(id_chks)%>%
mutate(Dataset = paste(as.character(dataset), Simulation),
Sex = ordered(Sex, levels = c("M", "F", "Mixed"),
labels = c("Male", "Female", "Mixed (all sexes)")))
Joining, by = c("Level", "Simulation")
n2 <- nrow(df_check)
if(n1 != n2)print("duplicates created in id-lut join")
df_check$Time.desc <- as.character(paste0("T=",df_check$Time))
df_check$Time.desc[df_check$Time.desc == "T=1e-06"] <- "SteadyState"
df_check$Dataset.Time <- interaction(df_check$Dataset,
df_check$Time.desc,lex.order=TRUE)
df_check$Dataset.Time <- factor(df_check$Dataset.Time,
levels=levels(df_check$Dataset.Time))
calibdata <- df_check[,names(df_check) != "Prediction"]
calibdata <- calibdata[!duplicated(calibdata),]
print(calibdata)
Level Simulation Output_Var Time Data Level1 dataset
1 1_1_1 1 Cserum_t 0.000000 82.400 1_1_1 Decatur M Train
2 1_1_1 1 Cserum_t 5.802000 70.300 1_1_1 Decatur M Train
3 1_1_2 2 Cserum_t 0.000000 32.600 1_1_2 Decatur M Train
4 1_1_2 2 Cserum_t 5.802000 14.200 1_1_2 Decatur M Train
5 1_1_3 3 Cserum_t 0.000000 236.000 1_1_3 Decatur M Train
6 1_1_3 3 Cserum_t 5.802000 75.400 1_1_3 Decatur M Train
7 1_1_4 4 Cserum_t 0.000000 61.000 1_1_4 Decatur M Train
8 1_1_4 4 Cserum_t 5.802000 12.800 1_1_4 Decatur M Train
9 1_1_5 5 Cserum_t 0.000000 182.000 1_1_5 Decatur M Train
10 1_1_5 5 Cserum_t 5.802000 43.900 1_1_5 Decatur M Train
11 1_1_6 6 Cserum_t 0.000000 25.300 1_1_6 Decatur M Train
12 1_1_6 6 Cserum_t 5.802000 18.800 1_1_6 Decatur M Train
13 1_1_7 7 Cserum_t 0.000000 113.000 1_1_7 Decatur M Train
14 1_1_7 7 Cserum_t 5.802000 24.000 1_1_7 Decatur M Train
15 1_1_8 8 Cserum_t 0.000000 78.200 1_1_8 Decatur M Train
16 1_1_8 8 Cserum_t 5.802000 26.400 1_1_8 Decatur M Train
17 1_1_9 9 Cserum_t 0.000000 54.400 1_1_9 Decatur M Train
18 1_1_9 9 Cserum_t 5.802000 26.500 1_1_9 Decatur M Train
19 1_1_10 10 Cserum_t 0.000000 81.200 1_1_10 Decatur F Train
20 1_1_10 10 Cserum_t 5.802000 31.500 1_1_10 Decatur F Train
21 1_1_11 11 Cserum_t 0.000000 70.700 1_1_11 Decatur F Train
22 1_1_11 11 Cserum_t 5.802000 50.200 1_1_11 Decatur F Train
23 1_1_12 12 Cserum_t 0.000000 13.700 1_1_12 Decatur F Train
24 1_1_12 12 Cserum_t 5.802000 12.800 1_1_12 Decatur F Train
25 1_1_13 13 Cserum_t 0.000000 42.000 1_1_13 Decatur F Train
26 1_1_13 13 Cserum_t 5.802000 28.100 1_1_13 Decatur F Train
27 1_1_14 14 Cserum_t 0.000000 98.000 1_1_14 Decatur F Train
28 1_1_14 14 Cserum_t 5.802000 35.100 1_1_14 Decatur F Train
29 1_1_15 15 Cserum_t 0.000000 56.900 1_1_15 Decatur F Train
30 1_1_15 15 Cserum_t 5.802000 45.900 1_1_15 Decatur F Train
31 1_1_16 16 Cserum_t 0.000000 32.500 1_1_16 Decatur F Train
32 1_1_16 16 Cserum_t 5.802000 13.300 1_1_16 Decatur F Train
33 1_1_17 17 Cserum_t 0.000000 60.500 1_1_17 Decatur F Train
34 1_1_17 17 Cserum_t 5.802000 27.600 1_1_17 Decatur F Train
35 1_1_18 18 Cserum_t 0.000000 43.800 1_1_18 Decatur F Train
36 1_1_18 18 Cserum_t 5.802000 34.700 1_1_18 Decatur F Train
37 1_2_1 19 Cserum_t 0.000000 64.100 1_2_1 Decatur M Test
38 1_2_1 19 Cserum_t 5.802000 15.000 1_2_1 Decatur M Test
39 1_2_2 20 Cserum_t 0.000000 89.600 1_2_2 Decatur M Test
40 1_2_2 20 Cserum_t 5.802000 24.700 1_2_2 Decatur M Test
41 1_2_3 21 Cserum_t 0.000000 74.700 1_2_3 Decatur M Test
42 1_2_3 21 Cserum_t 5.802000 39.800 1_2_3 Decatur M Test
43 1_2_4 22 Cserum_t 0.000000 68.400 1_2_4 Decatur M Test
44 1_2_4 22 Cserum_t 5.802000 30.000 1_2_4 Decatur M Test
45 1_2_5 23 Cserum_t 0.000000 72.900 1_2_5 Decatur M Test
46 1_2_5 23 Cserum_t 5.802000 32.200 1_2_5 Decatur M Test
47 1_2_6 24 Cserum_t 0.000000 78.100 1_2_6 Decatur M Test
48 1_2_6 24 Cserum_t 5.802000 45.400 1_2_6 Decatur M Test
49 1_2_7 25 Cserum_t 0.000000 24.100 1_2_7 Decatur M Test
50 1_2_7 25 Cserum_t 5.802000 15.400 1_2_7 Decatur M Test
51 1_2_8 26 Cserum_t 0.000000 60.900 1_2_8 Decatur M Test
52 1_2_8 26 Cserum_t 5.802000 22.000 1_2_8 Decatur M Test
53 1_2_9 27 Cserum_t 0.000000 137.000 1_2_9 Decatur M Test
54 1_2_9 27 Cserum_t 5.802000 70.700 1_2_9 Decatur M Test
55 1_2_10 28 Cserum_t 0.000000 26.600 1_2_10 Decatur F Test
56 1_2_10 28 Cserum_t 5.802000 15.200 1_2_10 Decatur F Test
57 1_2_11 29 Cserum_t 0.000000 120.000 1_2_11 Decatur F Test
58 1_2_11 29 Cserum_t 5.802000 61.700 1_2_11 Decatur F Test
59 1_2_12 30 Cserum_t 0.000000 60.900 1_2_12 Decatur F Test
60 1_2_12 30 Cserum_t 5.802000 22.500 1_2_12 Decatur F Test
61 1_2_13 31 Cserum_t 0.000000 41.100 1_2_13 Decatur F Test
62 1_2_13 31 Cserum_t 5.802000 12.400 1_2_13 Decatur F Test
63 1_2_14 32 Cserum_t 0.000000 39.200 1_2_14 Decatur F Test
64 1_2_14 32 Cserum_t 5.802000 12.800 1_2_14 Decatur F Test
65 1_2_15 33 Cserum_t 0.000000 18.100 1_2_15 Decatur F Test
66 1_2_15 33 Cserum_t 5.802000 13.400 1_2_15 Decatur F Test
67 1_2_16 34 Cserum_t 0.000000 19.400 1_2_16 Decatur F Test
68 1_2_16 34 Cserum_t 5.802000 16.800 1_2_16 Decatur F Test
69 1_2_17 35 Cserum_t 0.000000 21.500 1_2_17 Decatur F Test
70 1_2_17 35 Cserum_t 5.802000 11.800 1_2_17 Decatur F Test
71 1_2_18 36 Cserum_t 0.000000 53.800 1_2_18 Decatur F Test
72 1_2_18 36 Cserum_t 5.802000 30.600 1_2_18 Decatur F Test
73 1_2_19 37 Cserum_t 0.000000 16.000 1_2_19 Decatur F Test
74 1_2_19 37 Cserum_t 5.802000 6.700 1_2_19 Decatur F Test
75 1_3_1 38 Cbgd_Css 0.000001 13.000 1_3_1 Minnesota Train
76 1_3_2 39 Cbgd_Css 0.000001 50.000 1_3_2 Minnesota Train
77 1_3_3 40 Cbgd_Css 0.000001 45.000 1_3_3 Minnesota Train
78 1_3_4 41 Cbgd_Css 0.000001 55.000 1_3_4 Minnesota Train
79 1_3_5 42 Cbgd_Css 0.000001 58.000 1_3_5 Minnesota Train
80 1_3_6 43 Cbgd_Css 0.000001 50.000 1_3_6 Minnesota Train
81 1_3_7 44 Cbgd_Css 0.000001 150.000 1_3_7 Minnesota Train
82 1_3_8 45 Cbgd_Css 0.000001 12.000 1_3_8 Minnesota Train
83 1_3_9 46 Cbgd_Css 0.000001 58.000 1_3_9 Minnesota Train
84 1_3_10 47 Cbgd_Css 0.000001 21.000 1_3_10 Minnesota Train
85 1_3_11 48 Cbgd_Css 0.000001 19.000 1_3_11 Minnesota Train
86 1_3_12 49 Cbgd_Css 0.000001 25.000 1_3_12 Minnesota Train
87 1_3_13 50 Cbgd_Css 0.000001 4.000 1_3_13 Minnesota Train
88 1_3_14 51 Cbgd_Css 0.000001 32.000 1_3_14 Minnesota Train
89 1_3_15 52 Cbgd_Css 0.000001 58.000 1_3_15 Minnesota Train
90 1_3_16 53 Cbgd_Css 0.000001 8.500 1_3_16 Minnesota Train
91 1_3_17 54 Cbgd_Css 0.000001 5.500 1_3_17 Minnesota Train
92 1_3_18 55 Cbgd_Css 0.000001 58.000 1_3_18 Minnesota Train
93 1_3_19 56 Cbgd_Css 0.000001 50.000 1_3_19 Minnesota Train
94 1_3_20 57 Cbgd_Css 0.000001 145.000 1_3_20 Minnesota Train
95 1_3_21 58 Cbgd_Css 0.000001 77.000 1_3_21 Minnesota Train
96 1_3_22 59 Cbgd_Css 0.000001 50.000 1_3_22 Minnesota Train
97 1_3_23 60 Cbgd_Css 0.000001 90.000 1_3_23 Minnesota Train
98 1_3_24 61 Cbgd_Css 0.000001 14.000 1_3_24 Minnesota Train
99 1_3_25 62 Cbgd_Css 0.000001 21.000 1_3_25 Minnesota Train
100 1_3_26 63 Cbgd_Css 0.000001 35.000 1_3_26 Minnesota Train
101 1_3_27 64 Cbgd_Css 0.000001 28.000 1_3_27 Minnesota Train
102 1_3_28 65 Cbgd_Css 0.000001 7.000 1_3_28 Minnesota Train
103 1_3_29 66 Cbgd_Css 0.000001 150.000 1_3_29 Minnesota Train
104 1_3_30 67 Cbgd_Css 0.000001 50.000 1_3_30 Minnesota Train
105 1_3_31 68 Cbgd_Css 0.000001 50.000 1_3_31 Minnesota Train
106 1_3_32 69 Cbgd_Css 0.000001 70.000 1_3_32 Minnesota Train
107 1_3_33 70 Cbgd_Css 0.000001 21.000 1_3_33 Minnesota Train
108 1_3_34 71 Cbgd_Css 0.000001 19.000 1_3_34 Minnesota Train
109 1_3_35 72 Cbgd_Css 0.000001 40.000 1_3_35 Minnesota Train
110 1_3_36 73 Cbgd_Css 0.000001 70.000 1_3_36 Minnesota Train
111 1_3_37 74 Cbgd_Css 0.000001 45.000 1_3_37 Minnesota Train
112 1_3_38 75 Cbgd_Css 0.000001 22.000 1_3_38 Minnesota Train
113 1_3_39 76 Cbgd_Css 0.000001 29.000 1_3_39 Minnesota Train
114 1_3_40 77 Cbgd_Css 0.000001 28.000 1_3_40 Minnesota Train
115 1_3_41 78 Cbgd_Css 0.000001 6.500 1_3_41 Minnesota Train
116 1_3_42 79 Cbgd_Css 0.000001 22.000 1_3_42 Minnesota Train
117 1_3_43 80 Cbgd_Css 0.000001 21.000 1_3_43 Minnesota Train
118 1_3_44 81 Cbgd_Css 0.000001 41.000 1_3_44 Minnesota Train
119 1_3_45 82 Cbgd_Css 0.000001 41.000 1_3_45 Minnesota Train
120 1_3_46 83 Cbgd_Css 0.000001 16.000 1_3_46 Minnesota Train
121 1_3_47 84 Cbgd_Css 0.000001 70.000 1_3_47 Minnesota Train
122 1_3_48 85 Cbgd_Css 0.000001 16.000 1_3_48 Minnesota Train
123 1_3_49 86 Cbgd_Css 0.000001 30.000 1_3_49 Minnesota Train
124 1_4_1 87 Cbgd_Css 0.000001 3.000 1_4_1 Minnesota Test
125 1_4_2 88 Cbgd_Css 0.000001 8.700 1_4_2 Minnesota Test
126 1_4_3 89 Cbgd_Css 0.000001 9.000 1_4_3 Minnesota Test
127 1_4_4 90 Cbgd_Css 0.000001 11.000 1_4_4 Minnesota Test
128 1_4_5 91 Cbgd_Css 0.000001 15.000 1_4_5 Minnesota Test
129 1_4_6 92 Cbgd_Css 0.000001 16.000 1_4_6 Minnesota Test
130 1_4_7 93 Cbgd_Css 0.000001 40.000 1_4_7 Minnesota Test
131 1_4_8 94 Cbgd_Css 0.000001 26.000 1_4_8 Minnesota Test
132 1_4_9 95 Cbgd_Css 0.000001 18.000 1_4_9 Minnesota Test
133 1_4_10 96 Cbgd_Css 0.000001 20.000 1_4_10 Minnesota Test
134 1_4_11 97 Cbgd_Css 0.000001 35.000 1_4_11 Minnesota Test
135 1_4_12 98 Cbgd_Css 0.000001 41.000 1_4_12 Minnesota Test
136 1_4_13 99 Cbgd_Css 0.000001 12.000 1_4_13 Minnesota Test
137 1_4_14 100 Cbgd_Css 0.000001 15.000 1_4_14 Minnesota Test
138 1_4_15 101 Cbgd_Css 0.000001 18.000 1_4_15 Minnesota Test
139 1_4_16 102 Cbgd_Css 0.000001 20.000 1_4_16 Minnesota Test
140 1_4_17 103 Cbgd_Css 0.000001 25.000 1_4_17 Minnesota Test
141 1_4_18 104 Cbgd_Css 0.000001 38.000 1_4_18 Minnesota Test
142 1_4_19 105 Cbgd_Css 0.000001 160.000 1_4_19 Minnesota Test
143 1_4_20 106 Cbgd_Css 0.000001 32.000 1_4_20 Minnesota Test
144 1_4_21 107 Cbgd_Css 0.000001 7.000 1_4_21 Minnesota Test
145 1_4_22 108 Cbgd_Css 0.000001 28.000 1_4_22 Minnesota Test
146 1_4_23 109 Cbgd_Css 0.000001 40.000 1_4_23 Minnesota Test
147 1_4_24 110 Cbgd_Css 0.000001 12.000 1_4_24 Minnesota Test
148 1_4_25 111 Cbgd_Css 0.000001 80.000 1_4_25 Minnesota Test
149 1_4_26 112 Cbgd_Css 0.000001 90.000 1_4_26 Minnesota Test
150 1_4_27 113 Cbgd_Css 0.000001 22.000 1_4_27 Minnesota Test
151 1_4_28 114 Cbgd_Css 0.000001 50.000 1_4_28 Minnesota Test
152 1_4_29 115 Cbgd_Css 0.000001 21.000 1_4_29 Minnesota Test
153 1_4_30 116 Cbgd_Css 0.000001 60.000 1_4_30 Minnesota Test
154 1_4_31 117 Cbgd_Css 0.000001 61.000 1_4_31 Minnesota Test
155 1_4_32 118 Cbgd_Css 0.000001 120.000 1_4_32 Minnesota Test
156 1_4_33 119 Cbgd_Css 0.000001 18.000 1_4_33 Minnesota Test
157 1_4_34 120 Cbgd_Css 0.000001 35.000 1_4_34 Minnesota Test
158 1_4_35 121 Cbgd_Css 0.000001 68.000 1_4_35 Minnesota Test
159 1_4_36 122 Cbgd_Css 0.000001 35.000 1_4_36 Minnesota Test
160 1_4_37 123 Cbgd_Css 0.000001 53.000 1_4_37 Minnesota Test
161 1_4_38 124 Cbgd_Css 0.000001 35.000 1_4_38 Minnesota Test
162 1_4_39 125 Cbgd_Css 0.000001 57.000 1_4_39 Minnesota Test
163 1_4_40 126 Cbgd_Css 0.000001 58.000 1_4_40 Minnesota Test
164 1_4_41 127 Cbgd_Css 0.000001 71.000 1_4_41 Minnesota Test
165 1_4_42 128 Cbgd_Css 0.000001 65.000 1_4_42 Minnesota Test
166 1_4_43 129 Cbgd_Css 0.000001 18.000 1_4_43 Minnesota Test
167 1_4_44 130 Cbgd_Css 0.000001 40.000 1_4_44 Minnesota Test
168 1_4_45 131 Cbgd_Css 0.000001 26.000 1_4_45 Minnesota Test
169 1_4_46 132 Cbgd_Css 0.000001 90.000 1_4_46 Minnesota Test
170 1_4_47 133 Cbgd_Css 0.000001 91.000 1_4_47 Minnesota Test
171 1_4_48 134 Cbgd_Css 0.000001 180.000 1_4_48 Minnesota Test
172 1_4_49 135 Cbgd_Css 0.000001 130.000 1_4_49 Minnesota Test
173 1_5_1 136 M_Cbgd_Css 2.200000 7.690 1_5_1 Paulsboro-Train
174 1_6_1 137 M_Cbgd_Css 2.000000 24.639 1_6_1 Horsham-Train
175 1_7_1 138 M_Cbgd_Css 2.000000 21.378 1_7_1 Warminster-Test
176 1_8_1 139 M_Cbgd_Css 2.000000 20.754 1_8_1 Warrington-Train
Sex City Train_Test datatype variable
1 Male Decatur Train Individual Decatur M Train 1
2 Male Decatur Train Individual Decatur M Train 1
3 Male Decatur Train Individual Decatur M Train 2
4 Male Decatur Train Individual Decatur M Train 2
5 Male Decatur Train Individual Decatur M Train 3
6 Male Decatur Train Individual Decatur M Train 3
7 Male Decatur Train Individual Decatur M Train 4
8 Male Decatur Train Individual Decatur M Train 4
9 Male Decatur Train Individual Decatur M Train 5
10 Male Decatur Train Individual Decatur M Train 5
11 Male Decatur Train Individual Decatur M Train 6
12 Male Decatur Train Individual Decatur M Train 6
13 Male Decatur Train Individual Decatur M Train 7
14 Male Decatur Train Individual Decatur M Train 7
15 Male Decatur Train Individual Decatur M Train 8
16 Male Decatur Train Individual Decatur M Train 8
17 Male Decatur Train Individual Decatur M Train 9
18 Male Decatur Train Individual Decatur M Train 9
19 Female Decatur Train Individual Decatur F Train 10
20 Female Decatur Train Individual Decatur F Train 10
21 Female Decatur Train Individual Decatur F Train 11
22 Female Decatur Train Individual Decatur F Train 11
23 Female Decatur Train Individual Decatur F Train 12
24 Female Decatur Train Individual Decatur F Train 12
25 Female Decatur Train Individual Decatur F Train 13
26 Female Decatur Train Individual Decatur F Train 13
27 Female Decatur Train Individual Decatur F Train 14
28 Female Decatur Train Individual Decatur F Train 14
29 Female Decatur Train Individual Decatur F Train 15
30 Female Decatur Train Individual Decatur F Train 15
31 Female Decatur Train Individual Decatur F Train 16
32 Female Decatur Train Individual Decatur F Train 16
33 Female Decatur Train Individual Decatur F Train 17
34 Female Decatur Train Individual Decatur F Train 17
35 Female Decatur Train Individual Decatur F Train 18
36 Female Decatur Train Individual Decatur F Train 18
37 Male Decatur Test Individual Decatur M Test 19
38 Male Decatur Test Individual Decatur M Test 19
39 Male Decatur Test Individual Decatur M Test 20
40 Male Decatur Test Individual Decatur M Test 20
41 Male Decatur Test Individual Decatur M Test 21
42 Male Decatur Test Individual Decatur M Test 21
43 Male Decatur Test Individual Decatur M Test 22
44 Male Decatur Test Individual Decatur M Test 22
45 Male Decatur Test Individual Decatur M Test 23
46 Male Decatur Test Individual Decatur M Test 23
47 Male Decatur Test Individual Decatur M Test 24
48 Male Decatur Test Individual Decatur M Test 24
49 Male Decatur Test Individual Decatur M Test 25
50 Male Decatur Test Individual Decatur M Test 25
51 Male Decatur Test Individual Decatur M Test 26
52 Male Decatur Test Individual Decatur M Test 26
53 Male Decatur Test Individual Decatur M Test 27
54 Male Decatur Test Individual Decatur M Test 27
55 Female Decatur Test Individual Decatur F Test 28
56 Female Decatur Test Individual Decatur F Test 28
57 Female Decatur Test Individual Decatur F Test 29
58 Female Decatur Test Individual Decatur F Test 29
59 Female Decatur Test Individual Decatur F Test 30
60 Female Decatur Test Individual Decatur F Test 30
61 Female Decatur Test Individual Decatur F Test 31
62 Female Decatur Test Individual Decatur F Test 31
63 Female Decatur Test Individual Decatur F Test 32
64 Female Decatur Test Individual Decatur F Test 32
65 Female Decatur Test Individual Decatur F Test 33
66 Female Decatur Test Individual Decatur F Test 33
67 Female Decatur Test Individual Decatur F Test 34
68 Female Decatur Test Individual Decatur F Test 34
69 Female Decatur Test Individual Decatur F Test 35
70 Female Decatur Test Individual Decatur F Test 35
71 Female Decatur Test Individual Decatur F Test 36
72 Female Decatur Test Individual Decatur F Test 36
73 Female Decatur Test Individual Decatur F Test 37
74 Female Decatur Test Individual Decatur F Test 37
75 Mixed (all sexes) Minnesota Train Individual Minnesota Train 38
76 Mixed (all sexes) Minnesota Train Individual Minnesota Train 39
77 Mixed (all sexes) Minnesota Train Individual Minnesota Train 40
78 Mixed (all sexes) Minnesota Train Individual Minnesota Train 41
79 Mixed (all sexes) Minnesota Train Individual Minnesota Train 42
80 Mixed (all sexes) Minnesota Train Individual Minnesota Train 43
81 Mixed (all sexes) Minnesota Train Individual Minnesota Train 44
82 Mixed (all sexes) Minnesota Train Individual Minnesota Train 45
83 Mixed (all sexes) Minnesota Train Individual Minnesota Train 46
84 Mixed (all sexes) Minnesota Train Individual Minnesota Train 47
85 Mixed (all sexes) Minnesota Train Individual Minnesota Train 48
86 Mixed (all sexes) Minnesota Train Individual Minnesota Train 49
87 Mixed (all sexes) Minnesota Train Individual Minnesota Train 50
88 Mixed (all sexes) Minnesota Train Individual Minnesota Train 51
89 Mixed (all sexes) Minnesota Train Individual Minnesota Train 52
90 Mixed (all sexes) Minnesota Train Individual Minnesota Train 53
91 Mixed (all sexes) Minnesota Train Individual Minnesota Train 54
92 Mixed (all sexes) Minnesota Train Individual Minnesota Train 55
93 Mixed (all sexes) Minnesota Train Individual Minnesota Train 56
94 Mixed (all sexes) Minnesota Train Individual Minnesota Train 57
95 Mixed (all sexes) Minnesota Train Individual Minnesota Train 58
96 Mixed (all sexes) Minnesota Train Individual Minnesota Train 59
97 Mixed (all sexes) Minnesota Train Individual Minnesota Train 60
98 Mixed (all sexes) Minnesota Train Individual Minnesota Train 61
99 Mixed (all sexes) Minnesota Train Individual Minnesota Train 62
100 Mixed (all sexes) Minnesota Train Individual Minnesota Train 63
101 Mixed (all sexes) Minnesota Train Individual Minnesota Train 64
102 Mixed (all sexes) Minnesota Train Individual Minnesota Train 65
103 Mixed (all sexes) Minnesota Train Individual Minnesota Train 66
104 Mixed (all sexes) Minnesota Train Individual Minnesota Train 67
105 Mixed (all sexes) Minnesota Train Individual Minnesota Train 68
106 Mixed (all sexes) Minnesota Train Individual Minnesota Train 69
107 Mixed (all sexes) Minnesota Train Individual Minnesota Train 70
108 Mixed (all sexes) Minnesota Train Individual Minnesota Train 71
109 Mixed (all sexes) Minnesota Train Individual Minnesota Train 72
110 Mixed (all sexes) Minnesota Train Individual Minnesota Train 73
111 Mixed (all sexes) Minnesota Train Individual Minnesota Train 74
112 Mixed (all sexes) Minnesota Train Individual Minnesota Train 75
113 Mixed (all sexes) Minnesota Train Individual Minnesota Train 76
114 Mixed (all sexes) Minnesota Train Individual Minnesota Train 77
115 Mixed (all sexes) Minnesota Train Individual Minnesota Train 78
116 Mixed (all sexes) Minnesota Train Individual Minnesota Train 79
117 Mixed (all sexes) Minnesota Train Individual Minnesota Train 80
118 Mixed (all sexes) Minnesota Train Individual Minnesota Train 81
119 Mixed (all sexes) Minnesota Train Individual Minnesota Train 82
120 Mixed (all sexes) Minnesota Train Individual Minnesota Train 83
121 Mixed (all sexes) Minnesota Train Individual Minnesota Train 84
122 Mixed (all sexes) Minnesota Train Individual Minnesota Train 85
123 Mixed (all sexes) Minnesota Train Individual Minnesota Train 86
124 Mixed (all sexes) Minnesota Test Individual Minnesota Test 87
125 Mixed (all sexes) Minnesota Test Individual Minnesota Test 88
126 Mixed (all sexes) Minnesota Test Individual Minnesota Test 89
127 Mixed (all sexes) Minnesota Test Individual Minnesota Test 90
128 Mixed (all sexes) Minnesota Test Individual Minnesota Test 91
129 Mixed (all sexes) Minnesota Test Individual Minnesota Test 92
130 Mixed (all sexes) Minnesota Test Individual Minnesota Test 93
131 Mixed (all sexes) Minnesota Test Individual Minnesota Test 94
132 Mixed (all sexes) Minnesota Test Individual Minnesota Test 95
133 Mixed (all sexes) Minnesota Test Individual Minnesota Test 96
134 Mixed (all sexes) Minnesota Test Individual Minnesota Test 97
135 Mixed (all sexes) Minnesota Test Individual Minnesota Test 98
136 Mixed (all sexes) Minnesota Test Individual Minnesota Test 99
137 Mixed (all sexes) Minnesota Test Individual Minnesota Test 100
138 Mixed (all sexes) Minnesota Test Individual Minnesota Test 101
139 Mixed (all sexes) Minnesota Test Individual Minnesota Test 102
140 Mixed (all sexes) Minnesota Test Individual Minnesota Test 103
141 Mixed (all sexes) Minnesota Test Individual Minnesota Test 104
142 Mixed (all sexes) Minnesota Test Individual Minnesota Test 105
143 Mixed (all sexes) Minnesota Test Individual Minnesota Test 106
144 Mixed (all sexes) Minnesota Test Individual Minnesota Test 107
145 Mixed (all sexes) Minnesota Test Individual Minnesota Test 108
146 Mixed (all sexes) Minnesota Test Individual Minnesota Test 109
147 Mixed (all sexes) Minnesota Test Individual Minnesota Test 110
148 Mixed (all sexes) Minnesota Test Individual Minnesota Test 111
149 Mixed (all sexes) Minnesota Test Individual Minnesota Test 112
150 Mixed (all sexes) Minnesota Test Individual Minnesota Test 113
151 Mixed (all sexes) Minnesota Test Individual Minnesota Test 114
152 Mixed (all sexes) Minnesota Test Individual Minnesota Test 115
153 Mixed (all sexes) Minnesota Test Individual Minnesota Test 116
154 Mixed (all sexes) Minnesota Test Individual Minnesota Test 117
155 Mixed (all sexes) Minnesota Test Individual Minnesota Test 118
156 Mixed (all sexes) Minnesota Test Individual Minnesota Test 119
157 Mixed (all sexes) Minnesota Test Individual Minnesota Test 120
158 Mixed (all sexes) Minnesota Test Individual Minnesota Test 121
159 Mixed (all sexes) Minnesota Test Individual Minnesota Test 122
160 Mixed (all sexes) Minnesota Test Individual Minnesota Test 123
161 Mixed (all sexes) Minnesota Test Individual Minnesota Test 124
162 Mixed (all sexes) Minnesota Test Individual Minnesota Test 125
163 Mixed (all sexes) Minnesota Test Individual Minnesota Test 126
164 Mixed (all sexes) Minnesota Test Individual Minnesota Test 127
165 Mixed (all sexes) Minnesota Test Individual Minnesota Test 128
166 Mixed (all sexes) Minnesota Test Individual Minnesota Test 129
167 Mixed (all sexes) Minnesota Test Individual Minnesota Test 130
168 Mixed (all sexes) Minnesota Test Individual Minnesota Test 131
169 Mixed (all sexes) Minnesota Test Individual Minnesota Test 132
170 Mixed (all sexes) Minnesota Test Individual Minnesota Test 133
171 Mixed (all sexes) Minnesota Test Individual Minnesota Test 134
172 Mixed (all sexes) Minnesota Test Individual Minnesota Test 135
173 Mixed (all sexes) Paulsboro Train Summary Paulsboro-Train 136
174 Mixed (all sexes) Horsham Train Summary Horsham-Train 137
175 Mixed (all sexes) Warminster Test Summary Warminster-Test 138
176 Mixed (all sexes) Warrington Test Summary Warrington-Train 139
Dataset Time.desc Dataset.Time
1 Decatur M Train 1 T=0 Decatur M Train 1.T=0
2 Decatur M Train 1 T=5.802 Decatur M Train 1.T=5.802
3 Decatur M Train 2 T=0 Decatur M Train 2.T=0
4 Decatur M Train 2 T=5.802 Decatur M Train 2.T=5.802
5 Decatur M Train 3 T=0 Decatur M Train 3.T=0
6 Decatur M Train 3 T=5.802 Decatur M Train 3.T=5.802
7 Decatur M Train 4 T=0 Decatur M Train 4.T=0
8 Decatur M Train 4 T=5.802 Decatur M Train 4.T=5.802
9 Decatur M Train 5 T=0 Decatur M Train 5.T=0
10 Decatur M Train 5 T=5.802 Decatur M Train 5.T=5.802
11 Decatur M Train 6 T=0 Decatur M Train 6.T=0
12 Decatur M Train 6 T=5.802 Decatur M Train 6.T=5.802
13 Decatur M Train 7 T=0 Decatur M Train 7.T=0
14 Decatur M Train 7 T=5.802 Decatur M Train 7.T=5.802
15 Decatur M Train 8 T=0 Decatur M Train 8.T=0
16 Decatur M Train 8 T=5.802 Decatur M Train 8.T=5.802
17 Decatur M Train 9 T=0 Decatur M Train 9.T=0
18 Decatur M Train 9 T=5.802 Decatur M Train 9.T=5.802
19 Decatur F Train 10 T=0 Decatur F Train 10.T=0
20 Decatur F Train 10 T=5.802 Decatur F Train 10.T=5.802
21 Decatur F Train 11 T=0 Decatur F Train 11.T=0
22 Decatur F Train 11 T=5.802 Decatur F Train 11.T=5.802
23 Decatur F Train 12 T=0 Decatur F Train 12.T=0
24 Decatur F Train 12 T=5.802 Decatur F Train 12.T=5.802
25 Decatur F Train 13 T=0 Decatur F Train 13.T=0
26 Decatur F Train 13 T=5.802 Decatur F Train 13.T=5.802
27 Decatur F Train 14 T=0 Decatur F Train 14.T=0
28 Decatur F Train 14 T=5.802 Decatur F Train 14.T=5.802
29 Decatur F Train 15 T=0 Decatur F Train 15.T=0
30 Decatur F Train 15 T=5.802 Decatur F Train 15.T=5.802
31 Decatur F Train 16 T=0 Decatur F Train 16.T=0
32 Decatur F Train 16 T=5.802 Decatur F Train 16.T=5.802
33 Decatur F Train 17 T=0 Decatur F Train 17.T=0
34 Decatur F Train 17 T=5.802 Decatur F Train 17.T=5.802
35 Decatur F Train 18 T=0 Decatur F Train 18.T=0
36 Decatur F Train 18 T=5.802 Decatur F Train 18.T=5.802
37 Decatur M Test 19 T=0 Decatur M Test 19.T=0
38 Decatur M Test 19 T=5.802 Decatur M Test 19.T=5.802
39 Decatur M Test 20 T=0 Decatur M Test 20.T=0
40 Decatur M Test 20 T=5.802 Decatur M Test 20.T=5.802
41 Decatur M Test 21 T=0 Decatur M Test 21.T=0
42 Decatur M Test 21 T=5.802 Decatur M Test 21.T=5.802
43 Decatur M Test 22 T=0 Decatur M Test 22.T=0
44 Decatur M Test 22 T=5.802 Decatur M Test 22.T=5.802
45 Decatur M Test 23 T=0 Decatur M Test 23.T=0
46 Decatur M Test 23 T=5.802 Decatur M Test 23.T=5.802
47 Decatur M Test 24 T=0 Decatur M Test 24.T=0
48 Decatur M Test 24 T=5.802 Decatur M Test 24.T=5.802
49 Decatur M Test 25 T=0 Decatur M Test 25.T=0
50 Decatur M Test 25 T=5.802 Decatur M Test 25.T=5.802
51 Decatur M Test 26 T=0 Decatur M Test 26.T=0
52 Decatur M Test 26 T=5.802 Decatur M Test 26.T=5.802
53 Decatur M Test 27 T=0 Decatur M Test 27.T=0
54 Decatur M Test 27 T=5.802 Decatur M Test 27.T=5.802
55 Decatur F Test 28 T=0 Decatur F Test 28.T=0
56 Decatur F Test 28 T=5.802 Decatur F Test 28.T=5.802
57 Decatur F Test 29 T=0 Decatur F Test 29.T=0
58 Decatur F Test 29 T=5.802 Decatur F Test 29.T=5.802
59 Decatur F Test 30 T=0 Decatur F Test 30.T=0
60 Decatur F Test 30 T=5.802 Decatur F Test 30.T=5.802
61 Decatur F Test 31 T=0 Decatur F Test 31.T=0
62 Decatur F Test 31 T=5.802 Decatur F Test 31.T=5.802
63 Decatur F Test 32 T=0 Decatur F Test 32.T=0
64 Decatur F Test 32 T=5.802 Decatur F Test 32.T=5.802
65 Decatur F Test 33 T=0 Decatur F Test 33.T=0
66 Decatur F Test 33 T=5.802 Decatur F Test 33.T=5.802
67 Decatur F Test 34 T=0 Decatur F Test 34.T=0
68 Decatur F Test 34 T=5.802 Decatur F Test 34.T=5.802
69 Decatur F Test 35 T=0 Decatur F Test 35.T=0
70 Decatur F Test 35 T=5.802 Decatur F Test 35.T=5.802
71 Decatur F Test 36 T=0 Decatur F Test 36.T=0
72 Decatur F Test 36 T=5.802 Decatur F Test 36.T=5.802
73 Decatur F Test 37 T=0 Decatur F Test 37.T=0
74 Decatur F Test 37 T=5.802 Decatur F Test 37.T=5.802
75 Minnesota Train 38 SteadyState Minnesota Train 38.SteadyState
76 Minnesota Train 39 SteadyState Minnesota Train 39.SteadyState
77 Minnesota Train 40 SteadyState Minnesota Train 40.SteadyState
78 Minnesota Train 41 SteadyState Minnesota Train 41.SteadyState
79 Minnesota Train 42 SteadyState Minnesota Train 42.SteadyState
80 Minnesota Train 43 SteadyState Minnesota Train 43.SteadyState
81 Minnesota Train 44 SteadyState Minnesota Train 44.SteadyState
82 Minnesota Train 45 SteadyState Minnesota Train 45.SteadyState
83 Minnesota Train 46 SteadyState Minnesota Train 46.SteadyState
84 Minnesota Train 47 SteadyState Minnesota Train 47.SteadyState
85 Minnesota Train 48 SteadyState Minnesota Train 48.SteadyState
86 Minnesota Train 49 SteadyState Minnesota Train 49.SteadyState
87 Minnesota Train 50 SteadyState Minnesota Train 50.SteadyState
88 Minnesota Train 51 SteadyState Minnesota Train 51.SteadyState
89 Minnesota Train 52 SteadyState Minnesota Train 52.SteadyState
90 Minnesota Train 53 SteadyState Minnesota Train 53.SteadyState
91 Minnesota Train 54 SteadyState Minnesota Train 54.SteadyState
92 Minnesota Train 55 SteadyState Minnesota Train 55.SteadyState
93 Minnesota Train 56 SteadyState Minnesota Train 56.SteadyState
94 Minnesota Train 57 SteadyState Minnesota Train 57.SteadyState
95 Minnesota Train 58 SteadyState Minnesota Train 58.SteadyState
96 Minnesota Train 59 SteadyState Minnesota Train 59.SteadyState
97 Minnesota Train 60 SteadyState Minnesota Train 60.SteadyState
98 Minnesota Train 61 SteadyState Minnesota Train 61.SteadyState
99 Minnesota Train 62 SteadyState Minnesota Train 62.SteadyState
100 Minnesota Train 63 SteadyState Minnesota Train 63.SteadyState
101 Minnesota Train 64 SteadyState Minnesota Train 64.SteadyState
102 Minnesota Train 65 SteadyState Minnesota Train 65.SteadyState
103 Minnesota Train 66 SteadyState Minnesota Train 66.SteadyState
104 Minnesota Train 67 SteadyState Minnesota Train 67.SteadyState
105 Minnesota Train 68 SteadyState Minnesota Train 68.SteadyState
106 Minnesota Train 69 SteadyState Minnesota Train 69.SteadyState
107 Minnesota Train 70 SteadyState Minnesota Train 70.SteadyState
108 Minnesota Train 71 SteadyState Minnesota Train 71.SteadyState
109 Minnesota Train 72 SteadyState Minnesota Train 72.SteadyState
110 Minnesota Train 73 SteadyState Minnesota Train 73.SteadyState
111 Minnesota Train 74 SteadyState Minnesota Train 74.SteadyState
112 Minnesota Train 75 SteadyState Minnesota Train 75.SteadyState
113 Minnesota Train 76 SteadyState Minnesota Train 76.SteadyState
114 Minnesota Train 77 SteadyState Minnesota Train 77.SteadyState
115 Minnesota Train 78 SteadyState Minnesota Train 78.SteadyState
116 Minnesota Train 79 SteadyState Minnesota Train 79.SteadyState
117 Minnesota Train 80 SteadyState Minnesota Train 80.SteadyState
118 Minnesota Train 81 SteadyState Minnesota Train 81.SteadyState
119 Minnesota Train 82 SteadyState Minnesota Train 82.SteadyState
120 Minnesota Train 83 SteadyState Minnesota Train 83.SteadyState
121 Minnesota Train 84 SteadyState Minnesota Train 84.SteadyState
122 Minnesota Train 85 SteadyState Minnesota Train 85.SteadyState
123 Minnesota Train 86 SteadyState Minnesota Train 86.SteadyState
124 Minnesota Test 87 SteadyState Minnesota Test 87.SteadyState
125 Minnesota Test 88 SteadyState Minnesota Test 88.SteadyState
126 Minnesota Test 89 SteadyState Minnesota Test 89.SteadyState
127 Minnesota Test 90 SteadyState Minnesota Test 90.SteadyState
128 Minnesota Test 91 SteadyState Minnesota Test 91.SteadyState
129 Minnesota Test 92 SteadyState Minnesota Test 92.SteadyState
130 Minnesota Test 93 SteadyState Minnesota Test 93.SteadyState
131 Minnesota Test 94 SteadyState Minnesota Test 94.SteadyState
132 Minnesota Test 95 SteadyState Minnesota Test 95.SteadyState
133 Minnesota Test 96 SteadyState Minnesota Test 96.SteadyState
134 Minnesota Test 97 SteadyState Minnesota Test 97.SteadyState
135 Minnesota Test 98 SteadyState Minnesota Test 98.SteadyState
136 Minnesota Test 99 SteadyState Minnesota Test 99.SteadyState
137 Minnesota Test 100 SteadyState Minnesota Test 100.SteadyState
138 Minnesota Test 101 SteadyState Minnesota Test 101.SteadyState
139 Minnesota Test 102 SteadyState Minnesota Test 102.SteadyState
140 Minnesota Test 103 SteadyState Minnesota Test 103.SteadyState
141 Minnesota Test 104 SteadyState Minnesota Test 104.SteadyState
142 Minnesota Test 105 SteadyState Minnesota Test 105.SteadyState
143 Minnesota Test 106 SteadyState Minnesota Test 106.SteadyState
144 Minnesota Test 107 SteadyState Minnesota Test 107.SteadyState
145 Minnesota Test 108 SteadyState Minnesota Test 108.SteadyState
146 Minnesota Test 109 SteadyState Minnesota Test 109.SteadyState
147 Minnesota Test 110 SteadyState Minnesota Test 110.SteadyState
148 Minnesota Test 111 SteadyState Minnesota Test 111.SteadyState
149 Minnesota Test 112 SteadyState Minnesota Test 112.SteadyState
150 Minnesota Test 113 SteadyState Minnesota Test 113.SteadyState
151 Minnesota Test 114 SteadyState Minnesota Test 114.SteadyState
152 Minnesota Test 115 SteadyState Minnesota Test 115.SteadyState
153 Minnesota Test 116 SteadyState Minnesota Test 116.SteadyState
154 Minnesota Test 117 SteadyState Minnesota Test 117.SteadyState
155 Minnesota Test 118 SteadyState Minnesota Test 118.SteadyState
156 Minnesota Test 119 SteadyState Minnesota Test 119.SteadyState
157 Minnesota Test 120 SteadyState Minnesota Test 120.SteadyState
158 Minnesota Test 121 SteadyState Minnesota Test 121.SteadyState
159 Minnesota Test 122 SteadyState Minnesota Test 122.SteadyState
160 Minnesota Test 123 SteadyState Minnesota Test 123.SteadyState
161 Minnesota Test 124 SteadyState Minnesota Test 124.SteadyState
162 Minnesota Test 125 SteadyState Minnesota Test 125.SteadyState
163 Minnesota Test 126 SteadyState Minnesota Test 126.SteadyState
164 Minnesota Test 127 SteadyState Minnesota Test 127.SteadyState
165 Minnesota Test 128 SteadyState Minnesota Test 128.SteadyState
166 Minnesota Test 129 SteadyState Minnesota Test 129.SteadyState
167 Minnesota Test 130 SteadyState Minnesota Test 130.SteadyState
168 Minnesota Test 131 SteadyState Minnesota Test 131.SteadyState
169 Minnesota Test 132 SteadyState Minnesota Test 132.SteadyState
170 Minnesota Test 133 SteadyState Minnesota Test 133.SteadyState
171 Minnesota Test 134 SteadyState Minnesota Test 134.SteadyState
172 Minnesota Test 135 SteadyState Minnesota Test 135.SteadyState
173 Paulsboro-Train 136 T=2.2 Paulsboro-Train 136.T=2.2
174 Horsham-Train 137 T=2 Horsham-Train 137.T=2
175 Warminster-Test 138 T=2 Warminster-Test 138.T=2
176 Warrington-Train 139 T=2 Warrington-Train 139.T=2
#Multicheck plot
# Split Steady State Group into different populations for boxplot grouping
#df_check[df_check$Time.desc == "SteadyState" & grepl("Lubeck",df_check$Dataset),]$Time.desc <- "Lubeck"
#df_check[df_check$Time.desc == "SteadyState" & grepl("Little Hocking",df_check$Dataset),]$Time.desc <- "Little Hocking"
Modify aesthetics lookup table for boxplots
## additional source aesthetic lookup table for grey-scale time (years); merged legends save space on plotting output
times <- df_check%>% select(Time.desc, Time) %>% unique () %>%
mutate(rank = rank(Time) , grey = grey.colors(start=1,end=0.4, n = n()),
alpha = (rank)/8) %>%
select(-Time)
df_check <- df_check %>% mutate (legend_label = (paste0(City, "\n", Time.desc ) )) # add legend-labels
aes_lut <- df_check %>%
select(City, Train_Test, datatype,Time, Time.desc, legend_label) %>% unique () %>%
left_join(aes_lut[, c("City", "cols")], by = "City") %>% ungroup () %>% unique ()%>%
left_join (times, by = "Time.desc") %>%
arrange(datatype, City, Train_Test, Time) %>%
mutate(alpha = if_else(City == "Horsham", alpha/2, alpha)) %>% # otherwise too dark with this color
mutate_if(is.factor, as.character)
Changed grey start to 1 instead of 0.8, end at 0.6 instead of 0.4. Changed shape of symbols so they are filled.
#CD
# Decatur
df_decat <- df_check %>%
filter(City == "Decatur" & Train_Test %in% c ("Train", "Test")) %>%
mutate(panel = ordered (Train_Test, levels = c ("Train", "Test"),
labels = c("C: PFOS Decatur Train", "D: PFOS Decatur Test") ))
aes_lut_df_df_decat <- aes_lut %>%
filter(City == "Decatur" & Train_Test %in% c ("Train", "Test")) %>%
mutate_if(is.factor, as.character)
source( paste0(gsub(basename(here()), 'shared_functions', here()), '/plot_sum_boxplot.r'))
plt_train <- plot_sum_boxplot (dframe = df_decat, aes_lut= aes_lut_df_df_decat, facets = TRUE , pfas_nom = pfas_name )
print(plt_train)
ggsave(here ("output-plots",paste0( sa,"DecaturTrainTestboxplot",pfas_name,".pdf")),plt_train,dpi=600)
Saving 6.5 x 3.5 in image
Changed grey start to 1 instead of 0.8, end at 0.6 instead of 0.4. Added shapes and fills to data points.
lets <- LETTERS;
names(lets)[1:(length(unique(df_check$dataset))-4)]<-as.character(unique(df_check$dataset))[5:length(unique(df_check$dataset))]
for (d in unique(df_check$dataset)) { # d = unique(df_check$dataset)[11]
ddset <- df_check %>%
filter(dataset == d)
aes_lut_ddset <- ddset %>% select(legend_label, City,Train_Test,datatype, Time.desc ) %>% unique () %>% inner_join(aes_lut)
gt <- ifelse(is.na(lets[d]),d,paste0(lets[d],": ", d))
plt <- plot_sum_boxplot(dframe = ddset, aes_lut= aes_lut_ddset, gtitle= gt, facets = FALSE, pfas_nom = pfas_name)
print(plt)
ggsave(here ("output-plots",
paste0( sa, d,"-boxplot-",
pfas_name,".pdf")) ,
plt,dpi=600)
}
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
Warning: Column `City` joining factor and character vector, coercing into
character vector
Warning: Column `Train_Test` joining factor and character vector, coercing into
character vector
Saving 6.5 x 3.5 in image
### make Training plot
df_d_trt <- df_check %>%
filter( (Train_Test == "Train") & ((Output_Var == "M_Cbgd_Css") | (Output_Var == "M_Cserum"))) %>%
mutate_if(is.factor, as.character) %>% # drop factor levels unused
mutate(Dataset.Time = factor(Dataset.Time))
aes_lut_df_d_trt <- df_d_trt %>% select(City, datatype,Time, Time.desc, legend_label) %>%
inner_join(aes_lut ) %>%
select(-Train_Test) %>% ungroup () %>% unique ()
Joining, by = c("City", "datatype", "Time", "Time.desc", "legend_label")
plt_train <- plot_sum_boxplot(dframe = df_d_trt, aes_lut= aes_lut_df_d_trt,
gtitle="C: Summary Data - Train" , facets = FALSE,
pfas_nom = pfas_name )
print(plt_train)
ggsave(here ("output-plots", paste0( sa, "SummaryTrainDataboxplot",pfas_name,".pdf")), plt_train,dpi=600)
Saving 6.5 x 3.5 in image
### make Test plot
df_d_test <- df_check %>%
filter((Train_Test == "Test") &
((Output_Var == "M_Cbgd_Css") | (Output_Var == "M_Cserum"))) %>%
mutate_if(is.factor, as.character) %>% # drop factor levels unused
mutate(Dataset.Time = factor(Dataset.Time))
aes_lut_df_d_test <- df_d_test %>% select(City, datatype,Time, Time.desc, legend_label) %>%
inner_join(aes_lut ) %>%
select(-Train_Test) %>% ungroup () %>% unique ()
Joining, by = c("City", "datatype", "Time", "Time.desc", "legend_label")
plt_test <- plot_sum_boxplot(dframe = df_d_test, aes_lut= aes_lut_df_d_test,
gtitle="D: Summary Data - Test", facets = FALSE ,
pfas_nom = pfas_name)
print(plt_test)
ggsave(here ("output-plots",paste0( sa, "SummaryTestDataboxplot",pfas_name,".pdf")), plt_test,dpi=600)
Saving 6.5 x 3.5 in image
Shows shift in background estimate.
gmscale<-0.8
dat <- multicheck$parms.samp[,grep("M_ln_Cbgd",names(multicheck$parms.samp))]
datasetnames <- as.character(unique(calibdata$dataset))
datasetnames <- gsub(" M","",datasetnames)
datasetnames <- gsub(" F","",datasetnames)
datasetnames<-datasetnames[!duplicated(datasetnames)]
names(dat) <- datasetnames
dat <- dat[,grep("Train",names(dat))]
dat.df <- pivot_longer(dat,1:ncol(dat))
dat.df <- rbind(dat.df,
data.frame(name="Prior",value=rnorm(5000,m=log(gmscale),sd=0.4055)))
dat.df$name <- factor(dat.df$name,levels=rev(
c("Prior",datasetnames[grep("Train",datasetnames)])))
dat.df$value <- exp(dat.df$value)
p<-ggplot(dat.df)+
#geom_violin(aes(x=name,y=value,fill=name=="Prior"))+
geom_boxplot(aes(x=name,y=value,fill=name=="Prior"),outlier.shape=NA)+
scale_y_log10()+coord_flip()+
scale_fill_manual(name=NULL,
values=c("#009988", "#EE7733" )) +
theme_classic() +
geom_hline(yintercept = gmscale,color="grey")+
theme(legend.position="none",
panel.background = element_rect(color="black",size=1))+
ylab("Posterior shift in Background Concentration")
print(p)
ggsave(here ("output-plots",paste0( sa,"PFOS_GM_Cbgd.pdf")) , p, dpi=600)
Saving 5 x 6 in image
For PFOS, the population GM of the half-life has a posterior distribution that is narrower than the prior, with a posterior median (95% CI) estimate of 3.06 (2.16-4.37) years. The population GSD posterior is larger than the prior at 1.47(1.44-1.75).
dat <- multicheck$parms.samp[,c("M_ln_k.1.","V_ln_k.1.", "M_ln_Vd.1.", "SD_ln_Vd.1.")]
names(dat) <- c("M_ln_k(1)","V_ln_k(1)", "M_ln_Vd(1)", "SD_ln_Vd(1)")
set.seed(3.14159)
dat$z_ln_k <- rnorm(nrow(dat))
dat$z_ln_Vd <- rnorm(nrow(dat))
dat %>% rename_()
dat$ln_k_i <- dat$`M_ln_k(1)` + sqrt(dat$`V_ln_k(1)`)*dat$z_ln_k
dat$ln_Vd_i <- dat$`M_ln_Vd(1)`+ dat$`SD_ln_Vd(1)`*dat$z_ln_Vd
linmod <- lm(ln_Vd_i ~ ln_k_i,data=dat)
ggplot(dat) + geom_point(aes(ln_k_i,ln_Vd_i)) +
labs(subtitle=paste("Adj R2 =",signif(summary(linmod)$adj.r.squared,2)))
qqnorm(dat$ln_k_i,main="ln k Q-Q Normal")
qqline(dat$ln_k_i,col="red")
plot(ecdf(dat$ln_k_i))
x <- seq(-3,1,0.01)
m_ln_k_i <- mean(dat$ln_k_i)
sd_ln_k_i <- sd(dat$ln_k_i)
lines(x,pnorm(x,mean=m_ln_k_i,sd=sd_ln_k_i),col="red")
text(m_ln_k_i-2*sd_ln_k_i,0.9,paste("m =",signif(m_ln_k_i,4),"\nsd =",signif(sd_ln_k_i,4)))
qqnorm(dat$ln_Vd_i,main="ln Vd Q-Q Normal")
qqline(dat$ln_Vd_i,col="red")
plot(ecdf(dat$ln_Vd_i))
x <- seq(-3,1,0.01)
m_ln_Vd_i <- mean(dat$ln_Vd_i)
sd_ln_Vd_i <- sd(dat$ln_Vd_i)
lines(x,pnorm(x,mean=m_ln_Vd_i,sd=sd_ln_Vd_i),col="red")
text(m_ln_Vd_i-2*sd_ln_Vd_i,0.9,paste("m =",signif(m_ln_Vd_i,4),"\nsd =",signif(sd_ln_Vd_i,4)))
hl_i <- log(2)/ exp(dat$ln_k_i) # individual half-life
med_hl_i <- paste(signif (median (hl_i), 3)) # median of individual half-life
ci_med_hl_i <- paste(signif (quantile(hl_i, prob=c(0.025,0.975)), 3),collapse="-") # 95ci med individual halflife
gm_hl_i <- paste(signif (exp(mean(log(hl_i))), 3)) # gm (which should be really close)
gsd_hl_i <- paste(signif (exp(sd(log(hl_i))), 3)) # gsd individual
med_Vd_i <- paste(signif (median(exp(dat$ln_Vd_i)), 3)) # median individual Vd
ci_med_Vd_i <-paste(signif (quantile(exp(dat$ln_Vd_i), prob=c(0.025,0.975)), 3),collapse="-") # 95ci med individual Vd
gm_vd_i <- paste(signif (exp(mean(dat$ln_Vd_i)), 3)) # gm (which should be really close)
gsd_vd_i<- paste(signif (exp(sd(dat$ln_Vd_i)), 3)) # gsd indiv
PFOS_priors <- data.frame(
halflife_GM= log(2)/rlnorm(50000,
meanlog=-1.8971,sdlog=0.4055))
M_k <- exp(as.numeric(dat$`M_ln_k(1)`))
PFOS_halflife_GM <- log(2)/M_k
PFOS_hlgm_pr_med <- signif(median(PFOS_priors$halflife_GM,3))
PFOS_hlgm_pr_med_95ci <-paste(signif(quantile(PFOS_priors$halflife_GM,
prob=c(0.025,0.975)),
3),
collapse="-")
PFOS_hl_median_gm <- signif(median(PFOS_halflife_GM),3)
PFOS_hl_median_gm_95ci <- paste(signif(quantile(PFOS_halflife_GM,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(halflife_GM, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_halflife_GM,stat(density),color="Posterior"),geom="line",size=1.5 )+
xlim(0,15)+
labs(title = bquote("C: PFOS"~T[1/2]~"Population GM") ,
subtitle=paste("Posterior Median (95% CI): ",
PFOS_hl_median_gm," (",
PFOS_hl_median_gm_95ci,
")",sep=""))+
xlab(bquote("Population GM"~T[1/2]~"(yrs)")) +
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
Warning: Removed 80 rows containing non-finite values (stat_density).
ggsave(here ("output-plots",paste0( sa,"PFOS_hl_gm.pdf")), p, dpi=600)
Saving 4 x 2.5 in image
Warning: Removed 80 rows containing non-finite values (stat_density).
PFOS_priors$halflife_GSD = exp(sqrt(exp(rnorm(50000,m=log(0.2000),sd=log(1.275)))))
PFOS_halflife_GSD <- exp(sqrt(dat$`V_ln_k(1)`))
PFOS_hlgsd_pr_med <- signif(median(PFOS_priors$halflife_GSD,3))
PFOS_hlgsd_pr_med_95ci <-paste(signif(quantile(PFOS_priors$halflife_GSD,
prob=c(0.025,0.975)),
3),
collapse="-")
PFOS_hl_gsd_med <- signif(median(PFOS_halflife_GSD),3)
PFOS_hl_gsd_med_95ci <- paste(signif(quantile(PFOS_halflife_GSD,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(halflife_GSD, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_halflife_GSD,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(1,3)+
labs(title = bquote("D: PFOS"~T[1/2]~"Population GSD"),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_hl_gsd_med," (",
PFOS_hl_gsd_med_95ci,
")",sep=""))+
xlab(bquote("Population GSD"~T[1/2]))+
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" ))+
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_hl_gsd.pdf")), p, dpi=600)
For PFOS, the data were not particularly informative, but slightly increased the estimate of the median to 0.308(0.223-0.548) slightly. They were not informative as to the population GSD, with the posterior distributions essentially unchanged from the priors.
PFOS_priors$Vd_GM <- rlnorm(50000,
meanlog=-1.46968,
sdlog=0.2624)
PFOS_Vd_GM <- exp(dat$`M_ln_Vd(1)`)
PFOS_vd_gm_pr_med <- signif(median(PFOS_priors$Vd_GM,3))
PFOS_vd_gm_pr_med_95ci <- paste(signif(quantile(PFOS_priors$Vd_GM,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_vd_gm_med <- signif(median(PFOS_Vd_GM),3)
PFOS_vd_gm_med_95ci <- paste(signif(quantile(PFOS_Vd_GM,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(Vd_GM, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_Vd_GM,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(0,1)+labs(title = bquote("C: PFOS"~V[d]~"Population GM"),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_vd_gm_med," (",
PFOS_vd_gm_med_95ci,")",sep=""))+
xlab(bquote("Population GM"~V[d]~"(l/kg)"))+
scale_fill_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" )) + theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_vd_gm.pdf")), p, dpi=600)
PFOS_priors$Vd_GSD = exp(abs(rnorm(50000,sd=0.17)))
PFOS_Vd_GSD <- exp(dat$`SD_ln_Vd(1)`)
PFOS_vd_gsd_pr_med <- signif(median(PFOS_priors$Vd_GSD,3))
PFOS_vd_gsd_pr_med_95ci <- paste(signif(quantile(PFOS_priors$Vd_GSD,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_vd_gsd_med <- signif(median(PFOS_Vd_GSD),3)
PFOS_vd_gsd_med_95ci <- paste(signif(quantile(PFOS_Vd_GSD,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(Vd_GSD, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_Vd_GSD,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(1,3)+
labs(title = bquote("D: PFOS"~V[d]~"Population GSD "),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_vd_gsd_med," (",
PFOS_vd_gsd_med_95ci,
")",sep=""))+
xlab(bquote("Population GSD"~V[d]))+
scale_color_manual(name=NULL,
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_vd_gsd.pdf")), p, dpi=600)
Cl is k * Vd
PFOS_priors$CL_GM <- PFOS_priors$Vd_GM * (log(2)/PFOS_priors$halflife_GM)
PFOS_CL_GM <- exp(dat$`M_ln_Vd(1)` + dat$`M_ln_k(1)`)
PFOS_cl_gm_pr_med <- signif(median(PFOS_priors$CL_GM,3))
PFOS_cl_gm_pr_med_95ci <- paste(signif(quantile(PFOS_priors$CL_GM,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_cl_gm_med <- signif(median(PFOS_CL_GM),3)
PFOS_cl_gm_med_95ci <- paste(signif(quantile(PFOS_CL_GM,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(CL_GM, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_CL_GM,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(0,0.25)+labs(title = "B: PFOS Clearance Pop. GM ",subtitle=paste("Posterior Median (95% CI): ",
PFOS_cl_gm_med," (",
PFOS_cl_gm_med_95ci,
")",sep=""))+
xlab("Pop. GM CL (l/(kg-yr))")+
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_CL_gm.pdf")), p, dpi=600)
PFOS_hlgm_pr_med <- paste(signif(PFOS_hlgm_pr_med, 3))
PFOS_hl_median_gm<- paste(signif(PFOS_hl_median_gm, 3))
PFOS_hlgsd_pr_med<- paste(signif(PFOS_hlgsd_pr_med, 3))
PFOS_hl_gsd_med<- paste(signif(PFOS_hl_gsd_med, 3))
PFOS_vd_gm_pr_med<- paste(signif(PFOS_vd_gm_pr_med, 3))
PFOS_vd_gm_med<- paste(signif(PFOS_vd_gm_med, 3))
PFOS_vd_gsd_pr_med<- paste(signif(PFOS_vd_gsd_pr_med, 3))
PFOS_vd_gsd_med<- paste(signif(PFOS_vd_gsd_med, 3))
PFOS_cl_gm_pr_med<- paste(signif(PFOS_cl_gm_pr_med, 3))
PFOS_cl_gm_med<- paste(signif(PFOS_cl_gm_med, 3))
| Parameter | Prior GM | Posterior GM | Prior GSD | Posterior GSD |
|---|---|---|---|---|
| Half-life (years) | 4.62 | 3.36 | 1.56 | 1.57 |
| HL [95% CI] | [2.08-10.3] | [2.52-4.42] | [1.42-1.77] | [1.42-1.76] |
| Volume of distribution | 0.23 | 0.316 | 1.12 | 1.1 |
| \(V_D\) [95% CI] | [0.137-0.384] | [0.217-0.469] | [1.01-1.46] | [1.01-1.38] |
| Clearance | 0.0344 | 0.0663 | ||
| \(CL\) [95% CI] | [0.0133-0.0894] | [0.0473-0.09] | [] | [] |
| Parameter | median GM [95% CI] | GM calculator input | GSD individual |
|---|---|---|---|
| Half-life (years) | 3.4 [ 1.28-8.42 ] | 3.28 | 1.63 |
| Volume of distribution \(V_D\) | 0.319 [ 0.188-0.505 ] | 0.318 | 1.3 |
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.1 (2019-07-05)
os Red Hat Enterprise Linux Server 7.9 (Maipo)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2021-03-02
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
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[2] /opt/R/3.6.1/lib64/R/library